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Introduction to NLP
course content

Course Content

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
NLP and Its ApplicationsUnderstanding Text PreprocessingTokenizationChallenge: Tokenizing a Sentence Tokenization Using Regular ExpressionsChallenge: Tokenizing Using RegexRemoving Stop WordsApplying Text Preprocessing in Practice
2. Stemming and Lemmatization
StemmingChallenge: Stemming the TokensLemmatizationPart of Speech TaggingLemmatization with POS Tagging
3. Basic Text Models
Overview of Vector Space ModelsKey Types of Vector Space ModelsBag of WordsCustomizing Bag of WordsChallenge: Creating a Bag of WordsTF-IDFImplementing TF-IDF
4. Word Embeddings
Word Embeddings BasicsCBoW and Skip-gram Models (Optional)Implementing Word2VecChallenge: Creating Word Embeddings

bookRemoving Stop Words

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:


              
1234567

            
import nltk
from nltk.corpus import stopwords
# Download the stop words list
nltk.download('stopwords')
# Load English stop words
stop_words = set(stopwords.words('english'))
print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:


              
1234567891011121314

            
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('punkt_tab')
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
text = "This is an example sentence demonstrating the removal of stop words."
text = text.lower()
# Tokenize the text
tokens = word_tokenize(text)
# Remove stop words
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
print("Original Tokens:", tokens)
print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Task
test

Swipe to show code editor

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 7
toggle bottom row

bookRemoving Stop Words

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:


              
1234567

            
import nltk
from nltk.corpus import stopwords
# Download the stop words list
nltk.download('stopwords')
# Load English stop words
stop_words = set(stopwords.words('english'))
print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:


              
1234567891011121314

            
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('punkt_tab')
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
text = "This is an example sentence demonstrating the removal of stop words."
text = text.lower()
# Tokenize the text
tokens = word_tokenize(text)
# Remove stop words
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
print("Original Tokens:", tokens)
print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Task
test

Swipe to show code editor

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 7
toggle bottom row

bookRemoving Stop Words

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:


              
1234567

            
import nltk
from nltk.corpus import stopwords
# Download the stop words list
nltk.download('stopwords')
# Load English stop words
stop_words = set(stopwords.words('english'))
print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:


              
1234567891011121314

            
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('punkt_tab')
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
text = "This is an example sentence demonstrating the removal of stop words."
text = text.lower()
# Tokenize the text
tokens = word_tokenize(text)
# Remove stop words
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
print("Original Tokens:", tokens)
print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Task
test

Swipe to show code editor

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:


              
1234567

            
import nltk
from nltk.corpus import stopwords
# Download the stop words list
nltk.download('stopwords')
# Load English stop words
stop_words = set(stopwords.words('english'))
print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:


              
1234567891011121314

            
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('punkt_tab')
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
text = "This is an example sentence demonstrating the removal of stop words."
text = text.lower()
# Tokenize the text
tokens = word_tokenize(text)
# Remove stop words
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
print("Original Tokens:", tokens)
print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Task
test

Swipe to show code editor

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 7
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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